Wave Height Estimation From X-Band Nautical Radar Images Using Temporal Convolutional Network
Bibliographic record
Abstract
In this paper, a temporal convolutional network (TCN)-based model is proposed to retrieve significant wave height (Hs) from X-band nautical radar images. Three types of features are first extracted from radar image sequences based on signal to noise ratio (SNR), ensemble empirical mode decomposition (EEMD), and gray level co-occurrence matrix (GLCM) methods, respectively. Then, feature vectors are input into the proposed TCN-based regression model to produce Hs estimation. Radar data collected from a moving vessel at the East Coast of Canada, as well as the simultaneous wave data measured by several wave buoys deployed nearby are used for model training and testing. Experimental results after averaging show that TCN-based model further improves the Hs estimation accuracy, with reductions of root-mean-square errors (RMSEs) by 0.33 m and 0.10 m, respectively, compared to the SNR-based and the EEMD-based linear fitting methods. It has also been found that under the same feature extraction scheme, TCN outperforms other machine learning-based algorithms including support vector regression (SVR) and the gated recurrent unit (GRU) network.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".